Under conventional approaches, three-dimensional training data may be generated based on observations of distances to objects (e.g., measurements of a scene using LIDAR). Three-dimensional training data may be used to train tools to identify objects and/or behavior of objects. For example, LIDAR data may be used by a detection software to identify objects. However, observations of distances to objects may be imprecise and/or inaccurate. For instance, LIDAR data for a scene may include errors due to faulty LIDAR readings. Additionally, physical observations of objects within scenes may not include observations of particular objects/scenes. Training of tools using such data may lead to inaccurate detection of objects by the tools.